@ARTICLE{Zadeh1965338,
  author ="L.A. Zadeh",
  title ="Fuzzy Sets",
  journal ="Information and Control",
  year = "1965",
  volume =8,
  pages = "338--353",
}


@article{Pawlak1982341,
year={1982},
issn={0091-7036},
journal={International Journal of Computer and Information Sciences},
volume={11},
issue={5},
doi={10.1007/BF01001956},
title={Rough sets},
url={http://dx.doi.org/10.1007/BF01001956},
publisher={Kluwer Academic Publishers-Plenum Publishers},
keywords={Artificial intelligence; automatic classification; cluster analysis; fuzzy sets; inductive reasoning; learning algorithms; measurement theory; pattern recognition; tolerance theory},
author={Zdzis{\l}aw Pawlak},
pages={341-356},
language={English}
}


@ARTICLE{Dubois1989191,
  author =       {Didier Dubois and Henri Prade},
  title =        {Rough Fuzzy Sets and Fuzzy Rough Sets},
  journal =      {International Journal of General Systems},
  year =         {1989},
  volume =       {17},
  number =       {2},
  pages =        {191--209},
  month =        {Jul.},
  \*note =         {},
  abstract =     {The notion of a rough set introduced by Pawlak has often been compared to that of a fuzzy set,
sometimes with a view to prove that oneis more general, or, more useful than the other.Inthis paper
we arguethat both notions aim to different purposes. Seen this way, it is more natural to try to
combine the two models of uncertainty (vagueness and coarseness) rather than to have them compete
on the same problems. First, one may think of deriving the upper and lower approximations of afuzzy
set, when a reference scale is coarsened by means of an equivalence relation. We then come close to
Caianiello's C-calculus. Shafer's concept of coarsened belief functions also belongsto the same line of
thought. Another idea is to tum the equivalence relation into a fuzzy similarity relation, for the
modeling of coarseness, as already proposed by Fariiias del Cerro and Prade. Instead of using a
similarity relation, we can start withfuzzygranules which make afuzzypartition of the reference scale.
The maincontributionof the paper is to clarify the difference betweenfuzzysets and rough sets, and
unify several independent works which deal with similar ideas in different settings or notations.},
  keywords =     {Fuzzy sets, rough sets, C-calculus, random sets, belief functions, similarity relations},
}


@ARTICLE{Dubois1992203,
  author =       {Didier Dubois and Henri Prade},
  title =        {Putting rough sets and fuzzy sets together},
  year =         {1992},
  pages =        {203--232},
  note =         "in Intelligent Decision Support, Handbook of Applications and Advances of the Rough Sets Theory, R. Slwinski, Ed. Norway, MA: Kluwer",
}
